Mastering Custom Plot Layouts in R with ggplot2 and gtable

Introduction to Custom Plot Layouts in R

When working with data analysis, it’s common to create visualizations to understand and communicate insights. In this blog post, we’ll explore how to specify the size/layout of a single plot to match a certain grid in R using ggplot2 and gtable.

Background on Plotting in R

R provides an extensive range of libraries for data visualization, including ggplot2. ggplot2 is a powerful system for creating beautiful and publication-quality graphics. It uses a grammar-based approach, where the plot specification is divided into several parts: layers, aesthetics, and themes.

The ggplot() function is used to create a new plot. The basic syntax involves specifying the data source, the mapping of variables to aesthetics, and the layer types (e.g., points, bars).

Challenges with Existing Solutions

As mentioned in the question, exporting results as a CSV and visualizing them using Mac’s Numbers can be useful for certain workflows. However, when working directly within R, it’s often preferred to create plots programmatically.

The original code snippet provided by the question uses ggplot2 to create a bar chart with custom theme settings. The ggsave() function is used to export the plot as an image file.

Despite these efforts, the output plot doesn’t quite match the desired layout, which raises questions about whether it’s possible to specify the size/layout of a single plot in R.

Solving the Problem: Custom Plot Layouts with gtable

To achieve custom plot layouts, we can use gtable, an extension of grid graphics for creating complex layouts. The gtable package allows us to manipulate plots by transforming them into a more flexible data structure called gtable.

First, let’s explore how to create a basic gtable using ggplot2.

Creating a Basic Gtable

library(ggplot2)
library(gtable)
library(grid)

plt <- ggplot(mtcars, aes(factor(cyl), fill = factor(vs))) +
  geom_bar(position = position_dodge2(preserve = "single"))

# Making gtable
gt <- ggplotGrob(plt)

In this code snippet:

We load the necessary libraries.
We create a basic plot using ggplot2, specifying the data source and mapping variables to aesthetics.
We convert the plot into a gtable using ggplotGrob().
The resulting gtable contains several components: a grid that defines the plot’s structure.

Manipulating Gtable

Now that we have a basic gtable, let’s explore how to manipulate it to achieve our desired layout.

First, we need to understand where the relevant bits of our plot are in the gtable. In this case, the main panel and axes are located in cells 7-9 and 3-5 respectively.

Next, we can create a new gtable with specified dimensions and place these components into it.

# Making a new gtable
new <- gtable(widths = unit(c(12.5, 1.5, 4), "cm"),
              heights = unit(9, "cm"))

# Adding main panel and axes in first cell
new <- gtable_add_grob(
  new,
  gt[7:9, 3:5], # Main bits are in these rows/cols
  t = 1, l = 1
)

# Finding the legend
legend <- gt$grobs[gt$layout$name == "guide-box"][[1]]
legend <- legend$grobs[legend$layout$name == "guides"][[1]]

# Adding legend in third cell
new <- gtable_add_grob(
  new,
  legend, t = 1, l = 3
)

This code snippet:

Creates a new gtable with specified dimensions.
Places the main panel and axes into the first cell of the new gtable.
Finds the legend component in the original plot.
Adds the legend to the third cell of the new gtable.

Finally, we can save the modified gtable as an image file using the ragg::agg_png() function.

Last modified on 2025-03-17